
Readability and Visual Balance. When making posters by hand, designers prefer to place texts without underlays on relatively flat regions to ensure text readability and visual balance. Inspired by this, we propose \(R_{com}\)  to measure the layout quality. Firstly, we convert a test image to its grayscale, and calculate x- and y-direction gradients using the Sobel operator. The final gradient of a pixel is the root mean square of gradients for both directions. We define \(R_{com}\)  as the average gradients of the pixels covered by predicted text-only elements. Intuitively, a lower \(R_{com}\)  means better performance.

Presentation of Subjects. Attractive advertising posters should highlight the promoted products. For this, we figured out two evaluation ways, \(R_{sub}\)  and \(R_{shm}\) . To calculate \(R_{sub}\) , we get attention maps of promoted products (queried by their category tags extracted on product pages) on test images by CLIPhttps://github.com/hila-chefer/Transformer-MM-Explainability [1]}, [2]} and sum the attention values within layout regions. To calculate \(R_{shm}\) , we respectively feed the salient images with or without layout regions masked into a pretrained VGG16 [3]}, and calculate \({L_2}\)  distance between their output logits. \(R_{sub}\)  and \(R_{shm}\)  can reflect the occlusion levels of key subjects and the lower ones are better.

